1. Technical Field
The present disclosure relates generally to resource management in distributed computing environments, and more particularly, to methods for scaling down a classification computation by trading computational accuracy for computational resources.
2. Discussion of Related Art
An increasing number of sources of information are available due to the emergence of new sensor technologies. For example, various types of remote health monitoring technologies are being developed in the field of healthcare. In such settings, patients are surrounded by biomedical and environmental sensors able to collect enough data for medical professionals to continuously obtain detailed reports on the health of their patients.
Such remote monitoring systems may be highly distributed. They often adhere to a three tiered architecture: the sensor tier where data is collected, the hub tier where data is aggregated and normalized, and the server tier where data is analyzed. From a computational resource perspective, each tier has a very different profile. Indeed, at the sensor tier, power, central processing unit (CPU), memory and bandwidth resources are scarce. At the hub tier, more computational resources are available but not abundant. The server tier is by far the richer part of this architecture, in terms of computational resources. However, as more and more users are pumping data towards the server tier, it may also be operating under tight resource constraints.
These remote monitoring systems exchanges and process information. Hence, one may naturally refer to information theoretic concepts to model and optimize them. However, most applications of conventional information theory operate under three basic assumptions when attempting to transmit information in an efficient manner: (1) the encoder has access to an infinite amount of computational resources, (2) the encoding side of the communication system has more computational resources than the decoding end, and (3) the semantics of the messages transmitted are irrelevant to the transmission problem.
However, conventional information theory does not address the transmission of only parts of the message that are meaningful to the decoding end. Further, while these assumptions may hold for most broadcasting applications (e.g., digital video broadcasting), they fall short for sensor network and peer to peer applications. One can not assume the availability of large amounts of resources at the encoding end in network and peer to peer applications. There is a limited amount of computational resources available at the encoder and there is only an interest in the transmission of information that is relevant or important to a particular application. For example, if an abnormal pulse signal needs to be analyzed in the back end, there is no need for the sensor collecting pulse data to send readings that are in the normal ranges. Moreover, these application needs can change dynamically.
There is a need for adaptive techniques that are able to maximize the utility of the computation taking place, under dynamic resource constraints.